Discover how Qairnel profiles individuals to optimize trial efficiency and statistical power.
We identify the persons who are the most likely to:
– meet eligibility criteria
– show the highest treatment response
Using proprietary data from the DocMemo platform, we qualify trial volunteers with detailed risk and progression profiles. Qairnel proprietary AI identifies fast progressors at trial entry, enabling more powered trials.
Our AI/ML models analyze data collected throughout the DocMemo journey — including medical history, cognitive assessments and behavioral indicators — to predict biomarkers status, cognitive impairment, and trial readiness (e.g. likelihood to consent, suitability for protocol criteria).
These predictions optimize patient selection by ensuring that only qualified, likely-eligible volunteers are referred to trial clinics. This alleviates the need for sites to screen their active patient list and allows them to reallocate resources toward inclusion visits.
Qairnel’s proprietary generative AI models creates digital twins of trial participant at baseline. These models forecast disease progression over time, identifying fast vs. slow progressors. By accounting for inter-patient variability within the trial population, this approach reduces outcome variance and increases the statistical power of your trials.
While traditional phase 3 trial aim for 80% statistical power, integrating AI-generated forecasts alongside observed data can raise your actual chance of success from 80% to 97%. Alternatively, you can half the number of required participants while keeping power level at 80%.
Let’s discuss how Qairnel’s predictive models can help you optimize patient selection and reduce trial variability.